Quantifying the Amazon Isoprene Budget: Reconciling top-down versus bottom-up emission estimates

气候变化这个术语现在是一个家喻户晓的短语，我们已经习惯于听到温室气体水平上升和全球变暖。最早提高公众环境意识的事件之一是20世纪80年代南极臭氧层空洞的发现。臭氧是一种气体，它只占构成大气的所有气体的一小部分，但它在气候中非常重要。在高海拔(约15至30公里)，有大量的臭氧，这对地球是件好事，因为它屏蔽了太阳有害的紫外线辐射。然而，臭氧是一种有毒物质，如果它在对流层(大气层的最低部分)和表面积聚，那么这就不好了。对流层臭氧对我们有害，因为它(A)是一种温室气体，(B)是影响人类呼吸系统和农作物产量的空气污染物。当被称为挥发性有机化合物(VOC)的物质从表面排放出来并随后在大气中发生反应时，臭氧在表面附近产生。VOCs可以从人类活动中排放，但它们主要是由生长在陆地上的植物排放的。在排放到大气中的所有生物来源的VOCs中，没有一种比异戊二烯更重要，因为它能够与其他化合物快速反应(产生对流层臭氧)，而且它是大量排放的。异戊二烯也很重要，因为它是一种被称为二次有机气溶胶(SOA)的非常小的颗粒的来源，它会散射光线，影响地球变暖的方式，也会对健康造成不利影响。我们需要知道(A)何时、(B)在哪里和(C)有多少异戊二烯排放到大气中，以便更好地了解对流层臭氧和SOA。目前，我们使用基于观测的通用计算机模型来模拟不同类型的植被(如树木或草)排放的异戊二烯的数量。亚马逊盆地拥有世界上最大的雨林，被认为是最大的异戊二烯来源之一。由于很难测量这个基本上无法进入的偏远地区的排放量，亚马逊盆地的异戊二烯排放量很难量化。卫星对一种名为甲醛(HCHO)的气体的观测包含了异戊二烯排放的信息，可以用来确定陆地植被排放的异戊二烯的数量。我提议的总体目标是利用卫星对六六六的观测来准确量化亚马逊盆地的异戊二烯排放。为了实现这一目标，我将为亚马逊开发一个新的独特的高分辨率模式，它将能够在比以前更精细的空间尺度上模拟异戊二烯排放和大气化学。然后，我将把这个“自下而上”模型的异戊二烯排放量与从六六六卫星观测得出的“自上而下”的异戊二烯排放量进行比较，以找出模型和观测之间存在重大分歧的区域或时间段，这突显了我们对异戊二烯排放了解不足的地方。然后，我将开发一个改进的异戊二烯排放模型，通过将‘自下而上’的排放模型微调到推断的‘自上而下’的排放，并考虑到个别场景(利用嵌套网格的高空间分辨率)和不同的季节。通过协调“自下而上”模型和“自上而下”排放之间的差异，我们将更准确地估计亚马逊地区异戊二烯的排放量，更重要的是，我们将更好地了解影响其排放的因素。这项研究很重要，因为亚马逊盆地也是被确定为最容易受到气候变化影响的地区之一，而且至关重要的是，我们要确定影响其异戊二烯排放的关键因素，以提高人们对我们预测未来气候的能力的信心。

The term climate change is now a household phrase and we are used to hearing about rising greenhouse gas levels and global warming. One of the first events that increased the public's awareness of environmental issues was the discovery of the Antarctic ozone hole in the 1980s. Ozone is a gas that comprises only a tiny fraction of all the gases that make up the atmosphere but it is very important in climate. At high altitudes (about 15 to 30 km), there is lots of ozone which is good thing for the planet, as it shields the Sun's harmful UV radiation. However, ozone is a toxic substance and if it builds up within the troposphere (the lowermost part of the atmosphere) and at the surface then this is not good. Tropospheric ozone is bad for us because it is (a) a greenhouse gas, and (b) and air pollutant that affects the human respiratory system and agricultural crop yields. Ozone is produced near the surface when substances known as volatile organic compounds (VOCs) are emitted from the surface and subsequently react within the atmosphere. VOCs can be emitted from human activities, but they are predominantly emitted by vegetation that grows on land. Of all the biogenic VOCs emitted into the atmosphere, none is more important than isoprene owing to its ability to quickly react with other compounds (to produce tropospheric ozone) and because it is emitted in large amounts. Isoprene is also important, as it is a source of very small particles called secondary organic aerosol (SOA) that scatter light, which influences how the Earth warms, and which also have adverse health effects. We need to know (a) when, (b) where and (c) how much isoprene is emitted into the atmosphere in order to better understand tropospheric ozone and SOA. Currently we use generic computer models that are based on observations to simulate the amount of isoprene emitted from different types of vegetation, such as trees or grasses. Isoprene emissions from the Amazon Basin, which contains the world's largest rain forest and is thought to be one of the biggest isoprene sources, are poorly quantified since it is very difficult to measure the emissions in this largely inaccessible and remote region. Satellite observations of a gas called formaldehyde (HCHO), contain information on isoprene emissions, and can be used to determine the amount of isoprene emitted from terrestrial vegetation. The overall goal of my proposal is to use satellite observations of HCHO to accurately quantify isoprene emissions from the Amazon Basin. To achieve this goal I will develop a new unique high resolution model for the Amazon, which will be able to simulate isoprene emissions and atmospheric chemistry at finer spatial scales than have been able previously. I will then compare the isoprene emissions from this 'bottom-up' model with the 'top-down' isoprene emissions inferred from the satellite observations of HCHO, to identify regions or time periods where there is significant disagreement between the model and the observations, which highlights where we have poor understanding of the isoprene emissions. I will then develop an improved isoprene emission model by fine tuning the 'bottom-up' emission model to the inferred 'top-down' emissions, taking into account individual scenes (utilizing the high spatial resolution of the nested-grid) and different seasons. By reconciling the differences between the 'bottom-up' model and the 'top-down' emissions we will gain a more accurate estimate of how much isoprene is emitted from the Amazon, and more importantly gain a better understanding of the factors that influence when it is emitted. This research is important because the Amazon Basin is also one of the regions identified as being most susceptible to climate change, and it is crucial we determine the key factors that influence its isoprene emissions in order to improve confidence in our ability to predict future climate.